The invention relates generally to the field of gas turbine engine modeling. More specifically, the invention relates to methods and systems for adapting the measurement suite configuration of a gas turbine engine to provide robust performance tracking in light of sensor failures or data dropouts.
Gas turbine performance diagnostics concerns itself with tracking changes in engine module performance measures (typically efficiency and flow parameters) as the engine deteriorates over time. The primary sources of information driving this methodology are measurements taking along the engine's gas path, such as temperatures, pressures, speeds, etc. Tracking fleets of engines across a wide customer/aircraft base offers the added complexity that the measured parameters differ in instrumentation and recording fidelity and non-repeatability across installations.
Traditional performance diagnostic estimation methods employ some form of predictor/corrector estimation schemes. These procedures use the past performance estimation as a priori information for the current performance estimate calculation. Many of these approaches use linear estimation methods or derivatives of them to infer the performance changes from previous estimates and current data. The successful deployment of such diagnostic methods depends on many factors, one of which is its ability to adapt to different measurement suites without the need for employing complicated exception logic to cover all possible measurement scenarios.
A provision to provide some form of measurement configuration that will adapt itself to the measurement suite currently available and adapt to changes in this measurement set over the life of the engine monitoring program without requiring changes in the diagnostic software is a step to providing needed robustness in the performance tracking process. There are several factors that drive the need for such a measurement configuration process.
One factor is the fact that the number and types of gas path measurements available for conducting performance health trending, vary with the gas turbine model and type under consideration. For example, engines employing one or two spools, turbojet versus turbofan engines, mixed versus non-mixed flow, a new generation or a mature model engine, and others are factors that dictate what is and what is not available in the form of gas path instrumentation that provide the input parameter stream for the performance estimation process.
Many methods known in the art for performing engine module performance health tracking are generic in the sense that they may be applied to any type of gas turbine. Engine model specifics are the numeric model constants and the measurement suite available for the application. The former is typically a database issue whereas the latter may affect the actual software implementation of the process. A process that adapts to any specific measurement configuration would provide a greater degree of robustness and negate the need for software changes to implement a specific measurement set.
Another factor that drives the need for measurement configuration is that data dropouts are commonplace in aircraft engine monitoring. Parameters may, for whatever reason, disappear from the recorded input stream either intermittently for periods of time, or altogether. This may occur because of instrumentation problems, maintenance actions, recording anomalies, etc. Whatever the cause, the effective measurement suite changes as a result. If the performance estimation processing is dependent on a (pre-selected) measurement suite, the intermittent (or persistent) loss of one or more input parameters will cause a gap in the analysis to occur.
What is needed is a more robust engine performance tracking process that identifies the current time point measurement suite and adapts the measurement suite to changes to allow the performance estimation process to proceed.